脑信息处理动态特征研究

Dynamic Characteristics of Brain Information Processing

  • 摘要: 为提取脑信息处理过程中的动态特征参数,提出运用基于相空间重构思想的时间序列分维算法(G-P算法).讨论了G-P算法的3个重要参数(即无标度域、嵌入维数和延时)的确定规则,记录大脑在不同状态下的EEG信号并计算其关联维数.实验结果表明,EEG关联维数能够反映脑信息处理过程中的神经元群活动状态,可作为脑信息处理的非线性特征参数.

     

    Abstract: To acquire dynamic characteristics in brain information processing,a G-P algorithm based on the idea of restructuring of phase space is adopted.The rule of selecting three important parameters(non-graduation area,embedding dimension and delay) according to G-P algorithm is discussed.EEG signals under different brain condition are recorded and the correlation dimension is calculated.Experiments showed that the correlation dimension of EEG signal can reflect the active conditions of neuronal groups during brain information processing and can be used as the non-linear parameter of brain information processing.

     

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